U.S. patent number 8,068,938 [Application Number 12/466,648] was granted by the patent office on 2011-11-29 for method and system for managing a load demand on an electrical grid.
This patent grant is currently assigned to General Electric Company. Invention is credited to Lincoln Mamoru Fujita.
United States Patent |
8,068,938 |
Fujita |
November 29, 2011 |
Method and system for managing a load demand on an electrical
grid
Abstract
A method for managing electrical demand on a power grid in
response to electrical supply conditions is described. The method
includes determining a first energy demand forecast using stored
information, determining a first energy supply forecast based on a
known energy production and transmission capacity, and comparing
the first energy demand forecast to the first energy supply
forecast. The method also includes transmitting at least one of an
adjusted price signal and an electrical load shedding signal to a
customer over a bi-directional communication system based on the
comparison of the first energy demand forecast to the first energy
supply forecast.
Inventors: |
Fujita; Lincoln Mamoru
(Roanoke, VA) |
Assignee: |
General Electric Company
(Schenectady, NY)
|
Family
ID: |
43069186 |
Appl.
No.: |
12/466,648 |
Filed: |
May 15, 2009 |
Prior Publication Data
|
|
|
|
Document
Identifier |
Publication Date |
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US 20100292856 A1 |
Nov 18, 2010 |
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Current U.S.
Class: |
700/295; 702/61;
700/297; 702/60; 700/286; 700/291 |
Current CPC
Class: |
G06Q
50/06 (20130101); G06Q 10/04 (20130101); G06Q
10/06 (20130101); Y04S 10/50 (20130101); Y02P
90/80 (20151101) |
Current International
Class: |
G05D
3/12 (20060101); G05D 9/00 (20060101); G05D
11/00 (20060101); G05D 17/00 (20060101); G05D
5/00 (20060101); G01R 21/06 (20060101); G01R
21/00 (20060101) |
Field of
Search: |
;700/286,291,295,297
;702/60-61 |
References Cited
[Referenced By]
U.S. Patent Documents
Other References
Zakaria, Fareed,The Case for Brainy Power, Newsweek, Nov. 24, 2008,
2 pgs., http://www.newsweek.com/id/169165/output/print. cited by
other.
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Primary Examiner: Patel; Ramesh
Attorney, Agent or Firm: Armstrong Teasdale LLP
Claims
What is claimed is:
1. A method for managing electrical demand on a power grid in
response to electrical supply conditions, the method comprising:
determining a first energy demand forecast using stored
information, wherein the first energy demand forecast includes a
predicted energy usage over a predetermined time period;
determining a first energy supply forecast using at least a secure
unit commitment module and a generation capacity forecast module,
wherein the first energy supply forecast includes a predicted
energy supply over the predetermined time period; comparing the
first energy demand forecast to the first energy supply forecast;
and transmitting at least one of an adjusted price signal and an
electrical load shedding signal to a customer over a bi-directional
communication system based on the comparison of the first energy
demand forecast to the first energy supply forecast.
2. A method in accordance with claim 1 further comprising
calculating an adjusted price to be transmitted based at least
partially on the comparison of the first energy demand forecast to
the first energy supply forecast, wherein the adjusted price signal
notifies the customer of a price change.
3. A method in accordance with claim 1 wherein transmitting the
electrical load shedding signal comprises at least one of
transmitting a signal requesting that a customer remove an
electrical load from the power grid and transmitting a signal that
automatically removes the electrical load from the power grid.
4. A method in accordance with claim 1 wherein determining the
first energy demand forecast using stored information comprises
determining the first energy demand forecast using at least one of
historical weather information, historical demand information, and
weather forecast information.
5. A method in accordance with claim 1 further comprising:
determining a second energy demand forecast based on a customer
response to the adjusted price signal; determining a difference
between the second energy demand forecast and the first energy
supply forecast; and transmitting the electrical load shedding
signal to predetermined customers to shed a quantity of electrical
loads that is greater than the difference between the second energy
demand forecast and the first energy supply forecast.
6. A method in accordance with claim 5 further comprising:
determining a second energy supply forecast based on a customer
response to the adjusted price signal, wherein the second energy
supply forecast includes an increased energy supply during peak
time periods; and determining a difference between the second
energy demand forecast and the second energy supply forecast; and
transmitting the electrical load shedding signal to predetermined
customers to shed a quantity of electrical loads that is greater
than the difference between the second energy demand forecast and
the second energy supply forecast.
7. A method in accordance with claim 1 further comprising
generating a load shed schedule that rank orders loads available
for shedding.
8. A method in accordance with claim 1 wherein transmitting at
least one of the adjusted price signal and the electrical load
shedding signal to a customer over a bi-directional communication
system comprises sending at least one of the adjusted price signal
and the electrical load shedding signal to a customer over at least
one of a portion of an advanced metering infrastructure (AMI), a
wireless communication network and a broadband over power line
communication network.
9. A method in accordance with claim 1 wherein transmitting the
adjusted price signal to a customer further comprises transmitting
a request that a customer reduce energy usage over an upcoming
period of time.
10. A method in accordance with claim 1 wherein comparing the first
energy demand forecast to the first energy supply forecast
comprises: providing a graphical display of the first energy demand
forecast and first energy supply forecast to a power grid operator;
and providing the power grid operator with inputs for selection of
potential loads to be shed.
11. A system for managing electrical demand on a power grid in
response to electrical supply conditions comprising: a processing
device configured to: forecast a first energy demand forecast using
stored information, determine a first energy supply forecast using
at least a secure unit commitment module and a generation capacity
forecast module, compare the first energy demand forecast to the
first energy supply forecast, and provide a demand side management
(DSM) signal based at least partially on the comparison of the
first energy demand forecast and the first energy supply forecast;
and a bi-directional communication system communicatively coupling
said processing device to a plurality of customers, said
bi-directional communication system configured to receive the DSM
signal from said processing device and provide predetermined
customers of the plurality of customers with the DSM signal.
12. A system in accordance with claim 11 further comprising a
memory device communicatively coupled to said processing device,
said memory device configured to store the stored information in at
least one database.
13. A system in accordance with claim 11 wherein the stored
information comprises at least one of historical weather data,
historical demand data, and weather forecast data.
14. A system in accordance with claim 11 wherein the DSM signal
comprises at least one of an adjusted price signal and an
electrical load shedding signal, the adjusted price signal and the
electrical load shedding signal based at least partially on the
comparison of the first energy demand and the first energy
supply.
15. A system in accordance with claim 11 wherein said processing
device is further configured to calculate real-time pricing
information based at least partially on the comparison of the first
energy demand and the first energy supply.
16. A system in accordance with claim 11 wherein said processing
device is further configured to rank order loads available for
shedding.
17. A system in accordance with claim 11 wherein said
bi-directional communication system comprises at least one of a
wireless communication network and a broadband over power line
communication network.
18. A system in accordance with claim 11 wherein said
bi-directional communication system comprises an advanced metering
infrastructure (AMI) that couples said processing device to a DSM
system of a customer, said DSM system comprising at least one of a
DSM visualization device and a supervisory control and data
acquisition (SCADA) device.
19. A system in accordance with claim 18 wherein said DSM
visualization device is configured to display at least one of the
adjusted price and a load shed request to the customer.
20. A system in accordance with claim 18 wherein said SCADA device
is coupled to at least one customer load and configured to directly
control operation of the at least one customer load.
Description
BACKGROUND OF THE INVENTION
The field of the present disclosure relates generally to the
generation and delivery of electricity and more specifically, to a
method and system for managing peak electricity demand.
As energy demand around the world has increased, environmental
concerns and energy price volatility has increased interest in
energy conservation and in alternative energy sources. Programmable
thermostats have permitted consumers to program their heating and
cooling systems to reduce consumption during certain time periods,
for example, when they are not home or are asleep. Solar panels,
fuel cells, windmills, back-up generators, and other energy sources
have become increasingly available for use in residential homes and
businesses. However, the use of such alternative energy sources and
technologies may have been limited due to, for example, difficulty
in recovering costs, unpredictability of alternative energy
supplies (e.g., sun, wind, etc.), and/or a difficulty in
integrating such sources and technologies into conventional
electrical distribution systems.
Some electric utilities charge varying rates based on demand. For
example, during periods of peak demand, a higher rate for
electricity may be charged. Conversely, during low-demand periods,
a lower rate may be charged. The inability of some types of energy
users to curtail energy use and the lack of real-time information
regarding the immediate cost of energy usage may limit the success
of a variable rate program.
BRIEF DESCRIPTION OF THE INVENTION
In one aspect, a method for managing electrical demand on a power
grid in response to electrical supply conditions is provided. The
method includes determining a first energy demand forecast using
stored information, determining a first energy supply forecast
based on a known energy production and transmission capacity, and
comparing the first energy demand forecast to the first energy
supply forecast. The method also includes transmitting at least one
of an adjusted price signal and an electrical load shedding signal
to a customer over a bi-directional communication system based on
the comparison of the first energy demand forecast to the first
energy supply forecast.
In another aspect, a system for managing electrical demand on a
power grid in response to electrical supply conditions is provided.
The system includes a processing device and a bi-directional
communication system. The processing device is configured to
forecast a first energy demand forecast using stored information,
determine a first energy supply forecast based on known energy
production and transmission capacity, compare the first energy
demand forecast to the first energy supply forecast, and provide a
demand side management (DSM) signal based at least partially on the
comparison of the first energy demand forecast and the first energy
supply forecast. The bi-directional communication system
communicatively couples the processing device to a plurality of
customers. The bi-directional communication system is configured to
receive the DSM signal from the processing device and to provide
predetermined customers of the plurality of customers with the DSM
signal.
BRIEF DESCRIPTION OF THE DRAWINGS
FIG. 1 is a block diagram of an exemplary energy production and
transmission system.
FIGS. 2A and 2B show a block diagram of an exemplary demand side
management system.
FIG. 3 is a flow chart of an exemplary method for managing
electrical demand on a power grid in response to electrical supply
conditions.
FIGS. 4A and 4B show an exemplary DSM application flow chart that
further describes the method shown in FIG. 3.
FIG. 5 shows an example of a demand forecast that predicts an
amount of electricity that will be used over an upcoming
twenty-four hour time period.
FIG. 6 shows an example of an energy supply forecast that predicts
an amount of electricity that can be generated and delivered to
customers during the twenty-four hour time period illustrated in
FIG. 5.
FIG. 7 shows an example of the energy supply forecast of FIG. 6
overlaid on the demand forecast shown in FIG. 5.
FIG. 8 shows an exemplary adjusted power generation strategy.
DETAILED DESCRIPTION OF THE INVENTION
The embodiments described herein include an exemplary energy
production and transmission system for use in the generation and
delivery of electricity to customers. The embodiments described
herein facilitate the management of electrical demand in response
to electrical supply conditions.
A first technical effect of the energy production and transmission
system described herein is to provide direct control of loads
included within the transmission system. The first technical effect
is at least partially achieved by transmitting an electrical load
shedding signal to a customer over an advance metering
infrastructure (AMI). A second technical effect of the energy
production and transmission system described herein is to provide
indirect control of loads included within the transmission system.
The second technical effect is at least partially achieved by
transmitting an adjusted price signal to a customer over an
AMI.
FIG. 1 is a block diagram of an exemplary energy production and
transmission system 10 that includes an electric utility 12, a
power grid 14, and a plurality of customer locations 16, 18, and
20. Electricity is delivered from electric utility 12 to customer
locations 16, 18, and 20 via electric power grid 14. In the
exemplary embodiment, electric power grid 14 includes a plurality
of transmission lines 22 and an electrical substation 24. Electric
utility 12 includes an electric generator 26 that supplies
electrical power to power grid 14. Generator 26 may be driven by,
for example, a gas turbine engine, a hydroelectric turbine, and/or
a wind turbine. Electric utility 12 also includes a computer system
28 configured to control energy production and transmission.
Computer system 28 is illustrated as being included within electric
utility 12, however, computer system 28 may be external to electric
utility 12 (e.g., remotely located) and in communication with
electric utility 12. Furthermore, although described as a computer
system, computer system 28 may be any suitable processing device
that enables energy production and transmission system 10 to
function as described herein. In the exemplary embodiment, computer
system 28 is further configured a as part of a demand side
management (DSM) system, described in more detail below.
In the exemplary embodiment, customer locations 16, 18, and 20
include electric loads, for example, loads 40, 42, and 44.
Moreover, in the exemplary embodiment, customer locations 16, 18,
and 20 also include an end user meter 46. In the exemplary
embodiment, end user meter 46 is part of an advanced metering
infrastructure (AMI). The AMI is an example of a bidirectional
communication system that enables electric utility 12 to measure
and collect information relevant to energy usage from customer
locations 16, 18, and 20, as well as control loads 40, 42, and 44.
For example, using the AMI, electric utility 12 may prevent load 40
from receiving electricity from power grid 14, an operational
concept also referred to herein as "shedding" load 40 from power
grid 14. In an alternative embodiment, at least one load 40, 42,
and/or 44 may be a "smart device." As defined herein, smart devices
include a communication device that facilitates receiving a
shedding signal from electric utility 12 and turning-off the device
after receiving the shedding signal. Loads 40, 42, and 44 may be
communicatively coupled in any way that facilitates operation of
the AMI as described herein, three of which are shown within
customer locations 16, 18, and 20.
In the exemplary embodiment, end user meter 46 may include, or may
be coupled to, a display 50. For example, display 50 may include a
computer monitor or a liquid crystal display configured to display
an adjusted energy price and/or a request that the customer
turn-off a load during a suggested time period. Furthermore, the
AMI may be configured to provide the customer with the adjusted
energy price and/or request for removal of a load in any manner
that enables energy production and transmission system 10 to
function as described herein, for example, sending instructions by
e-mail, phone call, or text message.
FIGS. 2A and 2B show a block diagram of an exemplary DSM system
100. In the exemplary embodiment, DSM system 100 includes a core
DSM application 102. DSM system 100 also includes a network model
manager 110, a billing system 112, a customer information system
114, a decision support system 116, an outage management system
118, and an electrical grid supervisory control and data
acquisition (SCADA) system 120. Individual electrical customers 122
are connected via a bi-directional communication system, for
example, an advanced metering infrastructure (AMI) 124. In the
exemplary embodiment, demand side management application 102
includes a demand forecast module 130, a secure unit commitment
module 132, and a generation capacity forecast module 134. In the
exemplary embodiment, the functionality of DSM application 102,
network model manager 110, billing system 112, customer information
system 114, decision support system 116, and outage management
system 118 is performed by computer 28 (shown in FIG. 1). However,
the functions of DSM application 102, network model manager 110,
billing system 112, customer information system 114, decision
support system 116, and outage management system 118 may be
performed by multiple, centrally located computers, or multiple,
remotely located computers.
In the exemplary embodiment, demand forecast module 130 interfaces
with databases, for example, an historical demand database 140 and
an historical weather database 142, as well as with a real-time
weather database 144 and a real-time special event database 146. In
the exemplary embodiment, demand forecast module 130 uses data
stored in databases 140, 142, 144, and 146 to determine an
electrical demand forecast for an upcoming predetermined time
period. For example, demand forecast module 130 may determine an
electrical demand forecast that includes the expected energy demand
from customers, for example, customers 16, 18, and 20 (shown in
FIG. 1) for the upcoming month, the upcoming week, the next fifteen
minutes, or any predetermined future time period. Demand forecast
module 130 may also determine an electrical demand forecast for a
time period beginning at a future time, for example, an electrical
demand forecast for a day after the demand forecast is generated,
or an electrical demand forecast for an upcoming week beginning
three days after the demand forecast is generated. In the exemplary
embodiment, demand forecast module 130 may determine an electrical
demand forecast for any time period and/or delay desired, although
greater accuracy may be seen with more immediate forecasts. In the
exemplary embodiment, demand forecast module 130 forecasts a change
in energy demand over time, for example, an expected change in
energy demand at each hour of an upcoming day. In some embodiments,
demand forecast module 130 forecasts an aggregate demand amount,
over time, for the grid. In the exemplary embodiment, demand
forecast module 130 analyzes demand forecast accuracy by comparing
a past demand forecast with the actual electrical demand over the
same time period. In the exemplary embodiment, a database of these
analyses is maintained to increase the accuracy of future demand
forecasts.
In the exemplary embodiment, secure unit commitment module 132 and
generation capacity forecast module 134 provide an energy
generation forecast to DSM application 102. The energy generation
forecast includes a forecast of an expected level of energy
generation during a forecast time period. Secure unit commitment
module 132 receives available base load generation data 160, stored
power availability data 162, regulatory generation restraint data
164, real-time energy pricing signals 166, and/or data related to
access to real-time energy markets 168. In the exemplary
embodiment, secure unit commitment module 132 determines an initial
available energy generation forecast based at least in part on data
160, 162, 164, 166, and 168. Generation capacity forecast module
134 receives the initial available energy generation forecast from
secure unit commitment module 132 and modifies the initial
available energy generation forecast by applying planned
maintenance data 180 and emergency maintenance action data 182 to
determine a final energy generation forecast. For example,
generation capacity forecast module 134 may reduce the initial
available energy generation forecast at times associated with
planned maintenance actions on utility 12 (shown in FIG. 1). Along
with transmitting the final energy generation forecast to DSM
application 102, capacity generation forecast module 134 also saves
the forecast in a generation forecast database 184 to be used for
forecast accuracy analysis.
In the exemplary embodiment, network model manager 110 provides
network connectivity data to secure unit commitment module 132 and
DSM application 102. Moreover, in the exemplary embodiment, the
network connectivity data is used to determine an energy
transmission capability of power grid 14 (shown in FIG. 1). The
energy transmission capability includes an amount of electricity
that power grid 14 is capable of delivering to consumers 16, 18,
and 20 (shown in FIG. 1). In addition, in the exemplary embodiment,
network model manager 110 is common information model (CIM)
compliant and is configured to receive data from a bi-directional
communication system, for example, an intelligent grid and/or an
AMI system. Network model manager 110 receives the final energy
generation forecast from DSM application 102 and reduces the final
energy generation forecast to determine an energy supply forecast.
The final energy generation forecast may be reduced at times when
power grid 14 (shown in FIG. 1) is not capable of delivering the
level of electricity that generator 26 (shown in FIG. 1) is capable
of producing. In the exemplary embodiment, an energy supply
forecast map is created by applying grid energy transmission
constraints to the final available energy generation forecast.
In the exemplary embodiment, billing system 112 provides DSM
application 102 with real-time pricing information based at least
partially on data provided to billing system 112 by DSM application
102. For example, billing system 112 provides DSM application 102
with pricing information based on an energy demand forecast and an
energy supply forecast. In the exemplary embodiment, billing system
112 is configured for time of use (TOU) pricing. For example,
pricing may be determined by billing system 112 substantially
instantaneously. Additionally, pricing may be determined by billing
system 112 on a minute-by-minute basis, or any other term that
allows DSM application 102 to function as described herein. In some
embodiments billing system 112 may generate pricing information
that is characterized into discrete pricing groups, for example,
normal pricing for low use time periods, enhanced pricing for high
energy use time periods, and emergency pricing for critical energy
use time periods.
In the exemplary embodiment, customer information system 114
provides DSM application 102 with a list of critical loads and may
be configured to determine an outage rotation schedule for use when
DSM application 102 determines that an electrical load should be
removed, i.e., "shed," from power grid 14 (shown in FIG. 1). In an
alternative embodiment, customer information system 114 stores a
predetermined outage rotation schedule created by, for example, an
electrical utility operator.
In the exemplary embodiment, decision support system 116 receives
data corresponding to recommended actions determined by DSM
application 102 and transmits operator responses to DSM application
102. For example, data corresponding to a demand forecast, a supply
forecast, and/or an energy transmission capability may be provided
to decision support system 116 for use by an electric utility
operator. In the exemplary embodiment, recommended actions are
displayed by decision support system 116 along with a user
interface that receives operator instructions. For example, the
recommended actions may include transmitting an adjusted price
signal and/or an electrical load shedding signal to predetermined
customer locations and the user interface may enable the operator
to authorize, disapprove, or edit the recommended action.
In the exemplary embodiment, outage management system 118 receives
the planned outage rotation from DSM application 102 and
distinguishes planned outages from unplanned outages.
Distinguishing between planned outages and unplanned outages
facilitates preventing outage management system 118 from
dispatching maintenance crews to investigate outages that were
pre-planned and executed by the energy provider. Furthermore, SCADA
system 120 interfaces with DSM application 102, end user meters,
and/or smart home devices, for example, AMI meter 46 (shown in FIG.
1), to regulate individual loads. SCADA system 120 also tracks
voluntary end user load shedding and cases of end user load
shedding overrides to continually update the load shedding
schedule.
FIG. 3 is a flow chart 186 illustrating an exemplary method for
managing electrical demand on a power grid, for example, power grid
14 (shown in FIG. 1) in response to electrical supply conditions.
In the exemplary embodiment, the method includes determining 188 a
first energy demand forecast using stored information. The stored
information may include, for example, historical demand
information, historical weather information, real-time weather
information, and/or weather forecast information. Furthermore, as
described above, information stored in databases 140 and 142 (shown
in FIG. 2A) may be accessed by demand forecast module 130 (shown in
FIG. 2A) and used to determine 188 the first energy demand
forecast. The method also includes determining 190 a first energy
supply forecast based on a known energy production and transmission
capacity. For example, as described above, secure unit commitment
module 132 (shown in FIG. 2A) determines an initial energy
generation forecast based on available base load generation data
160, stored power availability data 162, regulatory generation
restraint data 164, real-time energy pricing signals 166, and/or
data related to access to real-time energy markets 168. Generation
capacity forecast module 134 receives the initial energy generation
forecast from secure unit commitment module 132 and modifies the
initial energy generation forecast by applying planned maintenance
data 180 and emergency maintenance action data 182 to determine the
final energy generation forecast. Network model manager 110
receives the final energy generation forecast from DSM application
102 and reduces the final energy generation forecast to determine
the first energy supply forecast.
In the exemplary embodiment, the method also includes comparing 192
the first energy demand forecast to the first energy supply
forecast and transmitting 194 at least one of an adjusted price
signal and an electrical load shedding signal to a customer over an
advanced metering infrastructure (AMI) when the first energy demand
forecast is greater than the first energy supply forecast for a
given time period. In some embodiments, comparing 192 the first
energy demand forecast to the first energy supply forecast may
include providing a graphical display of the first energy demand
and the first energy supply to a power grid operator and providing
the power grid operator with inputs for selection of potential
loads to be shed. For example, data corresponding to the first
demand forecast, the first supply forecast, and/or an energy
transmission capability of power grid 14 (shown in FIG. 1) may be
provided to decision support system 116 (shown in FIG. 2B) for use
by an operator. Recommended actions may be displayed by decision
support system 116 along with a user interface that facilitates
receiving operator instructions. For example, the recommended
actions may include transmitting 194 an adjusted price signal
and/or an electrical load shedding signal to predetermined customer
locations and the user interface may allow the operator to
authorize, disapprove, or edit the recommended action.
Information transmitted 194 may enable a calculated adjusted price
to be transmitted 194 as the adjusted price signal, based at least
partially on the comparison 192 of the first energy demand forecast
to the first energy supply forecast. The adjusted price signal
notifies the customer of a price change, and thus provides an
incentive for the customer to reduce electricity usage during
higher price time periods. Transmitting 194 may also include at
least one of transmitting 194 a signal requesting that a customer
remove an electrical load from the power grid or generally reduce
energy usage over an upcoming period of time, and transmitting 194
a signal that automatically removes an electrical load from the
power grid. For example, a customer and the electric utility may
have an agreement that the electric utility will charge the
customer a lower rate if the customer agrees to either manually
remove electrical loads from the power grid upon request, or allows
the electric utility to automatically remove electrical loads from
the power grid when the utility determines it would be beneficial
to reduce demand.
In some embodiments, transmitting 194 includes sending at least one
of the adjusted price signal and the electrical load shedding
signal to the customer over an AMI, for example, over at least one
of a wireless communication network and a broadband over power line
communication network.
In the exemplary embodiment, the method may also include
determining 196 a second energy demand forecast based on a customer
response to the adjusted price signal or the request to remove
electrical loads from the power grid. The method may also include
determining 198 a difference between the second energy demand
forecast and the first energy supply forecast and transmitting 200
the electrical load shedding signal to predetermined customers to
shed a quantity of electrical loads greater than the difference
between the second energy demand and the first energy supply. By
determining 198 the difference between the second energy demand
forecast and the first energy supply forecast, the method for
managing electrical demand on the power grid becomes an iterative
process, wherein transmitting 194 and 200 may be performed as many
times as is needed to achieve the desired demand forecast.
In some embodiments, the method may include generating 202 a load
shed schedule that rank orders loads available for shedding. For
example, customer information system 114 (shown in FIG. 2B) may
provide DSM application 102 (shown in FIG. 2B) with a load shed
schedule for use when DSM application 102 determines that an
electrical load should be shed. The load shed schedule may include
a list of critical loads that should not be shed. In the exemplary
embodiment, customer information system 114 generates the load shed
schedule. In an alternative embodiment, customer information system
114 may store a predetermined load shed schedule created by, for
example, an electrical utility operator.
FIGS. 4A and 4B show an exemplary DSM application flow chart 300,
that further describes the method shown in FIG. 3. For example, DSM
application 102 (shown in FIG. 2B) receives 302 a demand forecast
from, for example, demand forecast module 130 (shown in FIG. 2A),
and receives 304 an energy supply forecast from, for example,
secure unit commitment module 132 (shown in FIG. 2A). FIG. 5
illustrates an exemplary demand forecast that predicts an amount of
electricity that will be used (measured in megawatts, MW) over an
upcoming twenty-four hour time period. FIG. 6 illustrates an
exemplary energy supply forecast that predicts an amount of
electricity that can be generated and delivered to customers over
the same upcoming twenty-four hour time period. In the exemplary
embodiment, the demand forecast and energy supply forecast are
analyzed 306, for example, by comparing 308 the demand forecast to
the energy supply forecast. If demand is less than a corresponding
forecasted energy supply at all times within the demand forecast,
the method for managing electrical demand in response to electrical
supply conditions is complete.
In contrast, if at any time within the demand forecast the demand
is greater than the corresponding forecasted energy supply, in the
exemplary embodiment, DSM application 102 (shown in FIG. 2B) alerts
314 the utility operator. For example, FIG. 7 illustrates an
exemplary energy supply forecast (i.e., shown in FIG. 6) overlaid
upon an exemplary demand forecast (i.e., shown in FIG. 5). Circled
portions 310 and 312 illustrate exemplary time periods where the
forecasted demand is greater than the forecasted energy supply.
Alerting 314 the utility operator may include providing the
operator with a graphical representation of the demand forecast and
the energy supply forecast. Furthermore, DSM application 102 may
provide the operator with an option to adjust 316 a power
generation strategy. The adjusted power generation strategy may
include increasing a level of online generated electricity at
predetermined times to more completely match the energy supply
forecast to the demand forecast. FIG. 8 illustrates an exemplary
adjusted power generation strategy. After adjusting 316 power
generation, the demand may again be compared 318 with the energy
supply forecast. If at all times within the demand forecast the
demand is less than the forecasted electric supply, DSM application
102 provides 320 the operator with the option of whether or not to
execute the adjusted generation strategy. If the operator elects
not to execute the strategy, the method for managing electrical
demand in response to electrical supply conditions is complete.
Alternatively, the adjusted power generation strategy is executed
and the adjusted generation strategy is transmitted 322 to an
energy management system.
In the exemplary embodiment, DSM application 102 requests 330 a
processed list of controllable loads from, for example, customer
information system 114 (shown in FIG. 2B). In the exemplary
embodiment, from the list of controllable loads received from
customer information system 114, DSM application creates 332 an
optimized load dispatch schedule. Furthermore, DSM application 102
displays 334 recommended load shedding options to the operator,
wherein the operator may select 336 a desired option. Additionally,
the operator is provided 338 with the option to execute or not
execute the selected load shedding plan. If the operator selects
not to execute any of the recommended options, the method for
managing electrical demand in response to electrical supply
conditions is complete. Alternatively, DSM application 102
transmits 340 the load control schedule to, for example, SCADA 120
(shown in FIG. 2B) and/or OMS 118 (shown in FIG. 2B). DSM
application 102 also transmits 320 the adjusted generation strategy
to the energy management system and the method is complete.
In another aspect, a computer program embodied on a
computer-readable medium which stores a set of instructions is
provided. The computer program includes at least one code segment
that determines a first power grid energy demand forecast using
stored information, wherein the first power grid energy demand
forecast includes a predicted energy usage over a predetermined
time period. The computer program also includes at least one code
segment that determines a first power grid energy supply forecast
based on a known power grid energy production and transmission
capacity, wherein the first power grid energy supply forecast
includes a predicted energy supply over a predetermined time
period. Furthermore, at least one code segment compares the first
power grid energy demand forecast to the first power grid energy
supply forecast and transmits at least one of an adjusted price
signal and an electrical load shedding signal to a customer over a
bi-directional communication system when the first power grid
energy demand forecast and the first power grid energy supply
forecast indicate greater energy usage than energy supply. The
electrical load shedding signal may include at least one of a
signal requesting that a customer remove an electrical load from
the power grid, a signal that automatically removes the electrical
load from the power grid, and a signal providing notice to the
customer regarding a load being shed.
The computer program may also include at least one code segment
that calculates the adjusted price to be transmitted as the
adjusted price signal based at least partially on the comparison of
the first energy demand to the first energy supply. The adjusted
price signal may notify the customer of a price change. The
computer program may also include at least one code segment that
graphically displays the first energy demand and the first energy
supply to a power grid operator and receives power grid operator
input. The computer program may also determine a second power grid
energy demand based at least partially on customer response to the
adjusted price signal, transmit the electrical load shedding signal
to predetermined customers to shed a quantity of electrical loads
greater than the difference between the second energy demand and
the first energy supply, and transmit an electrical load reconnect
signal to at least one electrical load when the first energy demand
is less than the first energy supply. Furthermore, the computer
program may also generate a load shed schedule, wherein the load
shed schedule rank orders loads available for shedding.
As described herein, demand side management generally involves
voluntary load shedding, involuntary load shedding, or a
combination of the two. Voluntary load shedding may also be broken
into direct load control systems and rate-based load control
systems. Direct load control requires that the controllable loads
be enabled with a communication interface. This communication
interface may be a number of different alternatives. For example, a
Home Area Network (HAN) enables communication between an electric
utility and a controllable load. Examples of controllable loads
include programmable, digital thermostats, electric heaters, water
heaters, household appliances, and pool pumps.
The DSM system and method described herein determine whether DSM
actions are necessary to be implemented and the duration of those
actions. The determinations are provided to a utility operator to
alert them of a recommended DSM action in several "look-ahead"
periods such as a week ahead, a day ahead, and an hour ahead.
Through rules configured by the utility, a set of recommendations
are displayed to operators which may include a number of options
that will shed the appropriate amount of load required to meet the
energy supply forecast.
The DSM system and method described herein facilitate delivery of
direct load control signals by the SCADA system to controllable
loads through the AMI network. In the exemplary embodiment, these
signals are delivered to the AMI meter, to the HAN, and finally to
the specific loads that are being controlled.
In the method described herein, customers may "opt-out" of the load
control actions by simply overriding the control signals. For
example, if one of the actions is to adjust the temperature set
point of a thermostat a few degrees up or down, the consumer may
elect to override that setting manually.
The DSM system and method described herein facilitate implementing
direct load control at a level where the electrical service to a
customer is limited or discontinued. In a load-limiting scenario, a
demand limit setting is sent to the AMI meter. If the demand of the
customer is higher than the limit setting, the disconnect device in
the AMI meter will actuate to turn power off. After a preset time,
for example, five (5) minutes, the disconnect device will turn on.
If the demand continues to exceed the limit setting, the disconnect
device turns off, and then waits the preset amount of time. The
maximum amount of turn off-turn on cycles can be programmed in the
meter. If the maximum amount of cycles are reached, then the
disconnect device turns off power until the DSM actions are no
longer needed. Furthermore, after the DSM actions are no longer
needed, a signal from the SCADA system restores power to the
customer by closing the disconnect switch in the AMI meter.
Alternatively, a DSM action may include an instruction to turn-off
power to the customer throughout the entire DSM action period. This
may be accomplished through the disconnect switch in the AMI
meter.
The rate-based DSM system and method described herein may not allow
the utility to have any direct control over selected customer
loads. Instead, the utility may notify the customer that a rate
change will be in effect during a certain period of time. This
notification may be delivered through media channels (newspaper,
radio and television), through a direct contact means (telephone,
e-mail, pager systems), or through a signaling device such as a
specific in-premise display receiving rate signals through the AMI.
The customer has the discretion to modify their usage of energy.
The customer may elect to do so manually, by turning off appliances
and adjusting thermostats, or the consumer may elect to use a home
automation system to automatically curtail energy usage when higher
rates are in effect.
The term processing device, as used herein, refers to central
processing units, microprocessors, microcontrollers, reduced
instruction set circuits (RISC), application specific integrated
circuits (ASIC), logic circuits, and any other circuit, processor,
and/or computer capable of executing the functions described
herein.
As used herein, the terms "software" and "firmware" are
interchangeable, and include any computer program stored in memory
for execution, including RAM memory, ROM memory, EPROM memory,
EEPROM memory, and non-volatile RAM (NVRAM) memory. The above
memory types are exemplary only, and are thus not limiting as to
the types of memory usable for storage of a computer program.
As will be appreciated based on the foregoing specification, the
above-described embodiments of the disclosure may be implemented
using computer programming or engineering techniques including
computer software, firmware, hardware or any combination or subset
thereof, wherein the technical effect is for a method for managing
an electrical load demand on an electrical grid. Any such resulting
program, having computer-readable code means, may be embodied or
provided within one or more computer-readable media, thereby making
a computer program product, i.e., an article of manufacture,
according to the discussed embodiments of the disclosure. The
computer readable media may be, for example, but is not limited to,
a fixed (hard) drive, diskette, optical disk, magnetic tape,
semiconductor memory such as read-only memory (ROM), and/or any
transmitting/receiving medium such as the Internet or other
communication network or link. The article of manufacture
containing the computer code may be made and/or used by executing
the code directly from one medium, by copying the code from one
medium to another medium, or by transmitting the code over a
network.
This written description uses examples to disclose the invention,
including the best mode, and also to enable any person skilled in
the art to practice the invention, including making and using any
devices or systems and performing any incorporated methods. The
patentable scope of the invention is defined by the claims, and may
include other examples that occur to those skilled in the art. Such
other examples are intended to be within the scope of the claims if
they have structural elements that do not differ from the literal
language of the claims, or if they include equivalent structural
elements with insubstantial differences from the literal languages
of the claims.
* * * * *
References